Table REF  presents more quantitative results on both the Silhouettes and Google Fonts datasets. We compare both versions of our model (with and without the Domain-Confusion loss) with the VAE [1]}, [2]}, FactorVAE [3]}, \(\beta \) -TCVAE [4]}, and random guessing. For these models we use the same exact architecture and hyperparameters as with our Domain-Content model. For the FactorVAE we use the same discriminator network as in our model, without the summing and concatenation. We use a \(\beta =10\)  for the \(\beta \) -TCVAE and a \(\gamma =100\)  for the FactorVAE, reflecting the relative weighting that the discriminator loss has in our model. All the latent representations have dimensionality 16.
